






import os
from typing import BinaryIO, Optional, Union

import numpy as np
import pyarrow.parquet as pq

from .. import Audio, Dataset, Features, Image, NamedSplit, Value, config
from ..features.features import FeatureType, _visit
from ..formatting import query_table
from ..packaged_modules import _PACKAGED_DATASETS_MODULES
from ..packaged_modules.parquet.parquet import Parquet
from ..utils import logging
from ..utils.typing import NestedDataStructureLike, PathLike
from .abc import AbstractDatasetReader




def       _SCREAMING_SNAKE_CASE (							__snake_case )				->						Optional[int]:
       _UpperCAmelCase							=    np.inf

       def set_batch_size(__snake_case ) -> None:
              nonlocal batch_size
              if isinstance(__snake_case   ,      __snake_case ):
                     _UpperCAmelCase							=    min(__snake_case   ,      config.PARQUET_ROW_GROUP_SIZE_FOR_IMAGE_DATASETS )
              elif isinstance(__snake_case   ,      __snake_case ):
                     _UpperCAmelCase							=    min(__snake_case   ,      config.PARQUET_ROW_GROUP_SIZE_FOR_AUDIO_DATASETS )
              elif isinstance(__snake_case   ,      __snake_case ) and feature.dtype == "binary":
                     _UpperCAmelCase							=    min(__snake_case   ,      config.PARQUET_ROW_GROUP_SIZE_FOR_BINARY_DATASETS )

       _visit(__snake_case   ,      __snake_case )

       return None if batch_size is np.inf else batch_size
class 		SCREAMING_SNAKE_CASE__	(  UpperCAmelCase     ):

  '''simple docstring'''







  def __init__(      self		:			Optional[Any]       ,				lowerCamelCase		:			NestedDataStructureLike[PathLike]       ,				lowerCamelCase		:			Optional[NamedSplit] = None       ,				lowerCamelCase		:			Optional[Features] = None       ,				lowerCamelCase		:			str = None       ,				lowerCamelCase		:			bool = False       ,				lowerCamelCase		:			bool = False       ,				lowerCamelCase		:			Optional[int] = None       ,				**lowerCamelCase		:			Tuple       ,				)						->	Optional[Any]:


         """simple docstring"""
         super().__init__(
             lowerCamelCase       ,				split=lowerCamelCase       ,				features=lowerCamelCase       ,				cache_dir=lowerCamelCase       ,				keep_in_memory=lowerCamelCase       ,				streaming=lowerCamelCase       ,				num_proc=lowerCamelCase       ,				**lowerCamelCase       ,				)
         _UpperCAmelCase							=    path_or_paths if isinstance(lowerCamelCase       ,				lowerCamelCase							) else {self.split: path_or_paths}
         _UpperCAmelCase							=    _PACKAGED_DATASETS_MODULES["""parquet"""][1]
         _UpperCAmelCase							=    Parquet(
             cache_dir=lowerCamelCase       ,				data_files=lowerCamelCase       ,				features=lowerCamelCase       ,				hash=lowerCamelCase       ,				**lowerCamelCase       ,				)





  def  lowerCamelCase       (      self		:			Optional[Any]							)						->	List[str]:


         """simple docstring"""
         # Build iterable dataset
         if self.streaming:
                _UpperCAmelCase							=    self.builder.as_streaming_dataset(split=self.split							)
         # Build regular (map-style) dataset
         else:
                _UpperCAmelCase							=    None
                _UpperCAmelCase							=    None
                _UpperCAmelCase							=    None
                _UpperCAmelCase							=    None

                self.builder.download_and_prepare(
                    download_config=lowerCamelCase       ,				download_mode=lowerCamelCase       ,				verification_mode=lowerCamelCase       ,				base_path=lowerCamelCase       ,				num_proc=self.num_proc       ,				)
                _UpperCAmelCase							=    self.builder.as_dataset(
                    split=self.split       ,				verification_mode=lowerCamelCase       ,				in_memory=self.keep_in_memory							)
         return dataset



class 		SCREAMING_SNAKE_CASE__	:

  '''simple docstring'''







  def __init__(      self		:			List[str]       ,				lowerCamelCase		:			Dataset       ,				lowerCamelCase		:			Union[PathLike, BinaryIO]       ,				lowerCamelCase		:			Optional[int] = None       ,				**lowerCamelCase		:			Any       ,				)						->	Tuple:


         """simple docstring"""
         _UpperCAmelCase							=    dataset
         _UpperCAmelCase							=    path_or_buf
         _UpperCAmelCase							=    batch_size or get_writer_batch_size(dataset.features							)
         _UpperCAmelCase							=    parquet_writer_kwargs


  def  lowerCamelCase       (      self		:			List[Any]							)						->	int:


         """simple docstring"""
         _UpperCAmelCase							=    self.batch_size if self.batch_size else config.DEFAULT_MAX_BATCH_SIZE

         if isinstance(self.path_or_buf       ,				(str, bytes, os.PathLike)							):
                with open(self.path_or_buf       ,				"""wb+"""							) as buffer:
                       _UpperCAmelCase							=    self._write(file_obj=lowerCamelCase       ,				batch_size=lowerCamelCase       ,				**self.parquet_writer_kwargs							)
         else:
                _UpperCAmelCase							=    self._write(file_obj=self.path_or_buf       ,				batch_size=lowerCamelCase       ,				**self.parquet_writer_kwargs							)
         return written





  def  lowerCamelCase       (      self		:			Dict       ,				lowerCamelCase		:			BinaryIO       ,				lowerCamelCase		:			int       ,				**lowerCamelCase		:			List[str]							)						->	int:


         """simple docstring"""
         _UpperCAmelCase							=    0
         _UpperCAmelCase							=    parquet_writer_kwargs.pop("""path_or_buf"""       ,				lowerCamelCase							)
         _UpperCAmelCase							=    self.dataset.features.arrow_schema

         _UpperCAmelCase							=    pq.ParquetWriter(lowerCamelCase       ,				schema=lowerCamelCase       ,				**lowerCamelCase							)

         for offset in logging.tqdm(
             range(0       ,				len(self.dataset							)       ,				lowerCamelCase							)       ,				unit="""ba"""       ,				disable=not logging.is_progress_bar_enabled()       ,				desc="""Creating parquet from Arrow format"""       ,				):
                _UpperCAmelCase							=    query_table(
                    table=self.dataset._data       ,				key=slice(lowerCamelCase       ,				offset + batch_size							)       ,				indices=self.dataset._indices if self.dataset._indices is not None else None       ,				)
                writer.write_table(lowerCamelCase							)
                written += batch.nbytes
         writer.close()
         return written




'''simple docstring'''



def        UpperCAmelCase (    A	:					int       ,		A	:					int					):
							return x if y == 0 else greatest_common_divisor(A       ,		x % y					)
def        UpperCAmelCase (    A	:					int       ,		A	:					int					):
							return (x * y) // greatest_common_divisor(A       ,		A					)
def        UpperCAmelCase (    A	:					int = 20					):
							SCREAMING_SNAKE_CASE       :   Tuple         =      1
							for i in range(1       ,		n + 1					):
														SCREAMING_SNAKE_CASE       :   Optional[int]         =      lcm(A       ,		A					)
							return g


if __name__ == "__main__":
							print(f'{solution() = }')




